The Role of AI in Shaping Medical Education: Insights from an Umbrella Review of Review Studies.
Bibliographic record
Abstract
Introduction: Artificial intelligence (AI) has become integral to various fields, including medical education. This study explores AI applications in medical education through a review of relevant studies. Methods: Using the umbrella review method, this study synthesized findings from reviews conducted between 2018 and 2024. The PRISMA framework guided a comprehensive search of databases, including Science Direct, Springer, ERIC, PubMed, and Google Scholar. After quality assessment with the CASP framework, 77 systematic review articles were selected. Data analysis employed Elo and Kyngäs's qualitative content analysis approach, supported by expert validation and researcher consensus. Results: Six key themes of AI applications in medical education were identified: faculty, students, teaching and learning process, assessment, curriculum, and management/implementation. Management and implementation had the highest representation (26.5%), followed by teaching and learning processes (25.9%). Examples of each theme were highlighted. China produced the most articles, and three journals-International Journal of Educational Technology in Higher Education, Computers and Education: Artificial Intelligence, and Education and Information Technologies-were the leading publication venues. Conclusion: These six themes provide a roadmap for medical education policymakers to adapt to AI advancements. Emphasizing management and executive applications, the findings predict significant changes in the future of medical education and practice. This framework can help medical universities align curricula and operations with the evolving landscape of AI in healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.047 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".